This repository is a curated list of software, datasets, and standardization initiatives in materials informatics, targeting researchers and developers in materials science, computer science, and data science. It serves as a comprehensive resource for navigating the rapidly evolving landscape of data-driven materials discovery and design.

How It Works

The compilation categorizes efforts into Software and Products, Cloud Simulation Platforms and AI Startups, Machine-Readable Materials Datasets, and Standardization Initiatives. This structure allows users to quickly identify relevant tools, platforms, and data sources for their specific needs in computational materials science and AI-driven materials discovery.

Quick Start & Requirements

• Installation: Primarily involves cloning the repository and navigating through the listed resources. Specific software tools mentioned have their own installation procedures, often via pip or Docker.
• Prerequisites: Varies by tool, but common dependencies include Python, C++, and potentially specific scientific libraries or cloud platforms.
• Resources: Setup time and resource requirements are tool-dependent. Links to official documentation, demos, and quick-start guides are provided for many listed items.

Highlighted Details

• Extensive coverage of Python-based libraries like Pymatgen, ASE, and Matminer for data analysis and workflow management.
• Inclusion of major cloud simulation platforms and AI startups, highlighting commercial and academic efforts in accelerated materials discovery.
• Comprehensive listing of machine-readable materials datasets, including repositories like Materials Project, NOMAD, and AFLOW.
• Details on standardization initiatives such as OPTIMADE, CIF, and CML, crucial for data interoperability.

Maintenance & Community

The list is community-driven, with contributions welcomed. Specific maintainers or community links (e.g., Discord/Slack) are not explicitly detailed within the README, but the nature of an "awesome list" implies community curation.

Licensing & Compatibility

The repository itself is typically licensed under permissive terms (e.g., MIT), but the individual software, datasets, and platforms listed have a wide range of licenses, including proprietary, open-source (various licenses), and academic-use restrictions. Users must verify the licensing for each specific resource.

Limitations & Caveats

As a curated list, the content reflects the state of knowledge at the time of its last update and may not be exhaustive. The rapid pace of development in materials informatics means some entries might become outdated. Users must independently verify the status and applicability of each listed resource.